scholarly journals Real-time rainfall-runoff model of the Carraizo-reservoir basin in Puerto Rico

1996 ◽  
Keyword(s):  
Puerto Rico ◽  
Real Time ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Reservoir Basin ◽  
Runoff Model

Fuzzy computing based rainfall-runoff model for real time flood forecasting

2005 ◽  
Vol 19 (4) ◽  
pp. 955-968 ◽  
Author(s):  
P. C. Nayak ◽  
K. P. Sudheer ◽  
K. S. Ramasastri
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Fuzzy Computing

Hydrologic-hydraulic coupling for flash flood real-time simulation: Application to the October 2015 French Riviera floods

2020 ◽  
Author(s):  
Maryse Charpentier-Noyer ◽  
François Bourgin ◽  
Geoffroy Kirstetter ◽  
Olivier Delestre ◽  
Pierre Brigode
Keyword(s):  
Water Level ◽  
Real Time ◽  
Flash Floods ◽  
Rainfall Runoff ◽  
Calculation Time ◽  
Time Simulation ◽  
Hydraulic Coupling ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
French Riviera

<p>The vulnerability of the French Riviera to hydro meteorological hazards has been dramatically illustrated by the flash floods of October 3, 2015: 20 people were killed and the cost of the direct damages were higher than 600 million euros. Due to their fast dynamics, flash floods are difficult to predict and leave little time for forecasting. In this context, it is needed to improve real-time simulations to enable a short-range anticipation of the consequences of these phenomena. The main goal of this work was to test a hydrologic-hydraulic coupling in order to assess whether this coupling can be used for real-time forecasting purposes. The coupling is composed for the hydrological part of the event-based spatially distributed rainfall-runoff model Cinecar and for the hydraulic part of the Basilisk software, which is based on 2D hydraulic modelling (finite volume methods for shallow water equations) with adaptive grid refinement. The main interest of this coupling method is the compromise obtained between calculation time and precision. The rainfall-runoff model is run on the upstream part of the domain and feeds the hydraulic model applied in the downstream part. The rainfall-runoff model makes it possible to estimate very quickly the streamflow temporal evolution, while the hydraulic model, although much slower when applied at high spatial resolution (up to 4m), makes it possible to have water level and velocity at any point of the downstream area. The application of this coupling approach is presented for three basins severely affected by the October 2015 flash floods: the Brague (68 km²), the Frayère (22 km²) and the Riou de l’Argentière (48 km²) catchments. The results obtained for the three basins are compared with information gathered from post-event surveys, particularly the high water level marks. A particular attention is also put on computation times in order to evaluate the possibilities of real-time simulation. The results show promising performances both in terms of calculation time but also in terms of accuracy of the simulated flood areas and water levels.</p>

scholarly journals Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models

2002 ◽  
Vol 6 (4) ◽  
pp. 627-639 ◽  
Author(s):  
A. Brath ◽  
A. Montanari ◽  
E. Toth
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Analysis ◽  
Real Time ◽  
Rainfall Runoff ◽  
Rainfall Forecasting ◽  
Short Term ◽  
Analysis Techniques ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. Time-series analysis techniques for improving the real-time flood forecasts issued by a deterministic lumped rainfall-runoff model are presented. Such techniques are applied for forecasting the short-term future rainfall to be used as real-time input in a rainfall-runoff model and for updating the discharge predictions provided by the model. Along with traditional linear stochastic models, both stationary (ARMA) and non-stationary (ARIMA), the application of non-linear time-series models is proposed such as Artificial Neural Networks (ANNs) and the ‘nearest-neighbours’ method, which is a non-parametric regression methodology. For both rainfall forecasting and discharge updating, the implementation of each time-series technique is investigated and the forecasting schemes which perform best are identified. The performances of the models are then compared and the improvement in the efficiency of the discharge forecasts achievable is demonstrated when i) short-term rainfall forecasting is performed, ii) the discharge is updated and iii) both rainfall forecasting and discharge updating are performed in cascade. The proposed techniques, especially those based on ANNs, allow a remarkable improvement in the discharge forecast, compared with the use of heuristic rainfall prediction approaches or the not-updated discharge forecasts given by the deterministic rainfall-runoff model alone. Keywords: real-time flood forecasting, precipitation prediction, discharge updating, time-series analysis techniques

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